Results 81 to 90 of about 14,430,841 (247)

Controlling Grain Growth in Powder Bed Fusion of Yttria‐Stabilized Zirconia Using Femtosecond Lasers: Challenges and Methodological Insights

Advanced Engineering Materials, EarlyView.
Binder‐free laser powder bed fusion of 8YSZ with a femtosecond laser is used to map process windows linking scan strategy, heat accumulation, and grain growth. Time‐resolved thermography and simulations reveal thermal regimes that enable continuous, vitrified, and fine‐grained 8YSZ surface layers without absorptive additives and demonstrate ...
Markus Kühn, Sarah Kaden, Andrea Barz, Daniel Störzner, Jens Bliedtner, Andrés Fabián Lasagni   +5 more
wiley   +1 more source

Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty

IEEE Access
Accurate state-of-charge (SoC) estimation is crucial for enhancing the performance, longevity, safety, and reliability of lithium-ion batteries (LiBs) in electric vehicles (EVs).
Aya Haraz, Khalid Abualsaud, Ahmed M. Massoud   +2 more
doaj   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

Advanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin, Marta Campolucci, Nicolas de Andrade Ishiki, Nathan Mirgot, Nadir Zhamantay, Frédéric Kanoufi, Jennifer Peron, Jean Charlety, Emiliano Fonda, Valérie Briois, Marco Faustini, Maria‐Letizia De Marco, Christine Goyhenex, Ovidiu Ersen, Hervé Bulou   +14 more
wiley   +1 more source

Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning

Frontiers in Pharmacology
BackgroundChemicals may lead to acute liver injuries, posing a serious threat to human health. Achieving the precise safety profile of a compound is challenging due to the complex and expensive testing procedures.
Muhammad Zafar Irshad Khan, Jia-Nan Ren, Cheng Cao, Cheng Cao, Hong-Yu-Xiang Ye, Hao Wang, Ya-Min Guo, Jin-Rong Yang, Jin-Rong Yang, Jian-Zhong Chen   +9 more
doaj   +1 more source

Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning

Advanced Engineering Materials, EarlyView.
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose, Sanxchep Sharma, Veit Königer, Vincent Nebel, Jonas Oehm, Christian Weisenberger, Laura Bies, Volker Knoblauch   +7 more
wiley   +1 more source

Ensemble modeling of SARS-CoV-2 immune dynamics in immunologically naïve rhesus macaques predicts that potent, early innate immune responses drive viral elimination

Frontiers in Immunology
IntroductionAn unprecedented breadth of longitudinal viral and multi-scale immunological data has been gathered during SARS-CoV-2 infection. However, due to the high complexity, non-linearity, multi-dimensionality, mixed anatomic sampling, and possible ...
Catherine Byrne, Joshua T. Schiffer
doaj   +1 more source

Omnipolar Magnetic Field Detection by Superlattice‐Based Hall Sensor

Advanced Functional Materials, EarlyView.
Magnetic‐field‐induced electronic switching is demonstrated in unit‐cell‐engineered La0.7Sr0.3MnO3–BiFeO3 superlattices. Distinct substrate terminations modify magnetic and transport properties. Hall resistance measurements show omnipolar, hysteretic anomalous Hall switching above the Curie temperature, arising from Fe─Mn interfacial exchange, enabling
Mark Huijben, Nicolas Gauquelin, Arno Annys, Zhaoliang Liao, Jo Verbeeck, Guus Rijnders, Gertjan Koster   +6 more
wiley   +1 more source

Multiple-model based prediction of weekly discharge of the Brahmaputra-Jamuna by assimilating antecedent hydrological regime

Geocarto International
In hydrology, accurate predictions and monitoring of river discharge are critical for river engineering, flood mitigation, water resource management and agricultural purposes.
Md. Abdur Rahim, Shuang Liu, Kaiheng Hu, Hao Li, Md. Anwarul Abedin, Fatima Akter   +5 more
doaj   +1 more source

Historical Foundation and Practical Guideline for Ferroelectric Switching Kinetic Studies

Advanced Functional Materials, EarlyView.
The P and U pulses in the conventional PUND measurements are not identical because of the interplay between switching current and the measurement circuit components. This circuit effect can lead to a shift in polarization transients and misinterpreted physics in the switching kinetics.
Yi Liang, Pat Kezer, John T. Heron
wiley   +1 more source